Overview

Dataset statistics

Number of variables21
Number of observations5546880
Missing cells4147574
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory888.7 MiB
Average record size in memory168.0 B

Variable types

Categorical4
Numeric17

Alerts

DATEUTC has a high cardinality: 52560 distinct valuesHigh cardinality
ID has a high cardinality: 108 distinct valuesHigh cardinality
Date has a high cardinality: 366 distinct valuesHigh cardinality
LC_HUMIDITY is highly overall correlated with LC_RAD and 5 other fieldsHigh correlation
LC_DWPTEMP is highly overall correlated with LC_TEMP_QCL0 and 3 other fieldsHigh correlation
LC_RAD is highly overall correlated with LC_HUMIDITY and 1 other fieldsHigh correlation
LC_RAD60 is highly overall correlated with LC_HUMIDITY and 5 other fieldsHigh correlation
LC_TEMP_QCL0 is highly overall correlated with LC_HUMIDITY and 5 other fieldsHigh correlation
LC_TEMP_QCL1 is highly overall correlated with LC_HUMIDITY and 5 other fieldsHigh correlation
LC_TEMP_QCL2 is highly overall correlated with LC_HUMIDITY and 5 other fieldsHigh correlation
LC_TEMP_QCL3 is highly overall correlated with LC_HUMIDITY and 5 other fieldsHigh correlation
Year is highly imbalanced (> 99.9%)Imbalance
LC_HUMIDITY has 314899 (5.7%) missing valuesMissing
LC_DWPTEMP has 314899 (5.7%) missing valuesMissing
LC_n has 314899 (5.7%) missing valuesMissing
LC_RAD has 314899 (5.7%) missing valuesMissing
LC_RAININ has 314899 (5.7%) missing valuesMissing
LC_DAILYRAIN has 314899 (5.7%) missing valuesMissing
LC_WINDDIR has 314899 (5.7%) missing valuesMissing
LC_WINDSPEED has 314899 (5.7%) missing valuesMissing
LC_RAD60 has 277022 (5.0%) missing valuesMissing
LC_TEMP_QCL0 has 314899 (5.7%) missing valuesMissing
LC_TEMP_QCL1 has 345487 (6.2%) missing valuesMissing
LC_TEMP_QCL2 has 345487 (6.2%) missing valuesMissing
LC_TEMP_QCL3 has 345487 (6.2%) missing valuesMissing
LC_RAININ is highly skewed (γ1 = 46.73704986)Skewed
DATEUTC is uniformly distributedUniform
LC_RAD has 2610449 (47.1%) zerosZeros
LC_RAININ has 5068067 (91.4%) zerosZeros
LC_DAILYRAIN has 4333746 (78.1%) zerosZeros
LC_WINDDIR has 2022619 (36.5%) zerosZeros
LC_WINDSPEED has 2122964 (38.3%) zerosZeros
Hour has 231120 (4.2%) zerosZeros
Minute has 924480 (16.7%) zerosZeros
LC_RAD60 has 2542931 (45.8%) zerosZeros

Reproduction

Analysis started2023-04-19 09:53:39.657314
Analysis finished2023-04-19 09:58:05.255528
Duration4 minutes and 25.6 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

DATEUTC
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct52560
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size42.3 MiB
2022-07-02 12:10:00
 
108
2022-09-01 09:00:00
 
108
2022-09-01 07:20:00
 
108
2022-09-01 07:30:00
 
108
2022-09-01 07:40:00
 
108
Other values (52555)
5546340 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters105390720
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-01-01 00:10:00
2nd row2022-01-01 00:20:00
3rd row2022-01-01 00:30:00
4th row2022-01-01 00:40:00
5th row2022-01-01 00:50:00

Common Values

ValueCountFrequency (%)
2022-07-02 12:10:00 108
 
< 0.1%
2022-09-01 09:00:00 108
 
< 0.1%
2022-09-01 07:20:00 108
 
< 0.1%
2022-09-01 07:30:00 108
 
< 0.1%
2022-09-01 07:40:00 108
 
< 0.1%
2022-09-01 07:50:00 108
 
< 0.1%
2022-09-01 08:00:00 108
 
< 0.1%
2022-09-01 08:10:00 108
 
< 0.1%
2022-09-01 08:20:00 108
 
< 0.1%
2022-09-01 08:30:00 108
 
< 0.1%
Other values (52550) 5545800
> 99.9%

Length

2023-04-19T11:58:05.292949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20:50:00 38520
 
0.3%
23:20:00 38520
 
0.3%
13:40:00 38520
 
0.3%
13:50:00 38520
 
0.3%
14:00:00 38520
 
0.3%
14:10:00 38520
 
0.3%
14:20:00 38520
 
0.3%
14:30:00 38520
 
0.3%
14:40:00 38520
 
0.3%
14:50:00 38520
 
0.3%
Other values (500) 10708560
96.5%

Most occurring characters

ValueCountFrequency (%)
0 32888304
31.2%
2 22415940
21.3%
- 11093760
 
10.5%
: 11093760
 
10.5%
1 8739802
 
8.3%
5546880
 
5.3%
3 2876508
 
2.7%
5 2415744
 
2.3%
4 2400182
 
2.3%
7 1491264
 
1.4%
Other values (3) 4428576
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77656320
73.7%
Dash Punctuation 11093760
 
10.5%
Other Punctuation 11093760
 
10.5%
Space Separator 5546880
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32888304
42.4%
2 22415940
28.9%
1 8739802
 
11.3%
3 2876508
 
3.7%
5 2415744
 
3.1%
4 2400182
 
3.1%
7 1491264
 
1.9%
8 1491264
 
1.9%
6 1475712
 
1.9%
9 1461600
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 11093760
100.0%
Other Punctuation
ValueCountFrequency (%)
: 11093760
100.0%
Space Separator
ValueCountFrequency (%)
5546880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 105390720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32888304
31.2%
2 22415940
21.3%
- 11093760
 
10.5%
: 11093760
 
10.5%
1 8739802
 
8.3%
5546880
 
5.3%
3 2876508
 
2.7%
5 2415744
 
2.3%
4 2400182
 
2.3%
7 1491264
 
1.4%
Other values (3) 4428576
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105390720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32888304
31.2%
2 22415940
21.3%
- 11093760
 
10.5%
: 11093760
 
10.5%
1 8739802
 
8.3%
5546880
 
5.3%
3 2876508
 
2.7%
5 2415744
 
2.3%
4 2400182
 
2.3%
7 1491264
 
1.4%
Other values (3) 4428576
 
4.2%

ID
Categorical

Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.3 MiB
LC-002
 
52560
LC-104
 
52560
LC-100
 
52560
LC-099
 
52560
LC-097
 
52560
Other values (103)
5284080 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters33281280
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLC-002
2nd rowLC-002
3rd rowLC-002
4th rowLC-002
5th rowLC-002

Common Values

ValueCountFrequency (%)
LC-002 52560
 
0.9%
LC-104 52560
 
0.9%
LC-100 52560
 
0.9%
LC-099 52560
 
0.9%
LC-097 52560
 
0.9%
LC-096 52560
 
0.9%
LC-095 52560
 
0.9%
LC-094 52560
 
0.9%
LC-092 52560
 
0.9%
LC-091 52560
 
0.9%
Other values (98) 5021280
90.5%

Length

2023-04-19T11:58:05.334160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lc-002 52560
 
0.9%
lc-031 52560
 
0.9%
lc-004 52560
 
0.9%
lc-005 52560
 
0.9%
lc-006 52560
 
0.9%
lc-008 52560
 
0.9%
lc-009 52560
 
0.9%
lc-010 52560
 
0.9%
lc-011 52560
 
0.9%
lc-012 52560
 
0.9%
Other values (98) 5021280
90.5%

Most occurring characters

ValueCountFrequency (%)
L 5546880
16.7%
C 5546880
16.7%
- 5546880
16.7%
0 5085360
15.3%
1 3116160
9.4%
2 1498320
 
4.5%
3 1184400
 
3.6%
4 1143360
 
3.4%
7 985680
 
3.0%
6 985680
 
3.0%
Other values (3) 2641680
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16640640
50.0%
Uppercase Letter 11093760
33.3%
Dash Punctuation 5546880
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5085360
30.6%
1 3116160
18.7%
2 1498320
 
9.0%
3 1184400
 
7.1%
4 1143360
 
6.9%
7 985680
 
5.9%
6 985680
 
5.9%
9 933120
 
5.6%
8 933120
 
5.6%
5 775440
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
L 5546880
50.0%
C 5546880
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 5546880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22187520
66.7%
Latin 11093760
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 5546880
25.0%
0 5085360
22.9%
1 3116160
14.0%
2 1498320
 
6.8%
3 1184400
 
5.3%
4 1143360
 
5.2%
7 985680
 
4.4%
6 985680
 
4.4%
9 933120
 
4.2%
8 933120
 
4.2%
Latin
ValueCountFrequency (%)
L 5546880
50.0%
C 5546880
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33281280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 5546880
16.7%
C 5546880
16.7%
- 5546880
16.7%
0 5085360
15.3%
1 3116160
9.4%
2 1498320
 
4.5%
3 1184400
 
3.6%
4 1143360
 
3.4%
7 985680
 
3.0%
6 985680
 
3.0%
Other values (3) 2641680
7.9%

LC_HUMIDITY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct88
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean76.606512
Minimum12
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:05.388809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile41
Q164
median82
Q392
95-th percentile99
Maximum99
Range87
Interquartile range (IQR)28

Descriptive statistics

Standard deviation18.543738
Coefficient of variation (CV)0.24206478
Kurtosis-0.42356597
Mean76.606512
Median Absolute Deviation (MAD)12
Skewness-0.74770265
Sum4.0080381 × 108
Variance343.87024
MonotonicityNot monotonic
2023-04-19T11:58:05.441533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 332470
 
6.0%
96 160179
 
2.9%
95 158226
 
2.9%
94 156788
 
2.8%
97 153372
 
2.8%
93 150826
 
2.7%
92 147876
 
2.7%
91 141544
 
2.6%
90 141079
 
2.5%
98 137983
 
2.5%
Other values (78) 3551638
64.0%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
12 1
 
< 0.1%
13 40
 
< 0.1%
14 223
 
< 0.1%
15 457
 
< 0.1%
16 649
< 0.1%
17 656
< 0.1%
18 683
< 0.1%
19 732
< 0.1%
20 1110
< 0.1%
21 1517
< 0.1%
ValueCountFrequency (%)
99 332470
6.0%
98 137983
2.5%
97 153372
2.8%
96 160179
2.9%
95 158226
2.9%
94 156788
2.8%
93 150826
2.7%
92 147876
2.7%
91 141544
2.6%
90 141079
2.5%

LC_DWPTEMP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3560
Distinct (%)0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean8.1499152
Minimum-12.6
Maximum59.78
Zeros4444
Zeros (%)0.1%
Negative433125
Negative (%)7.8%
Memory size42.3 MiB
2023-04-19T11:58:05.495322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-12.6
5-th percentile-1.44
Q14.26
median8.76
Q312.28
95-th percentile16.27
Maximum59.78
Range72.38
Interquartile range (IQR)8.02

Descriptive statistics

Standard deviation5.4557662
Coefficient of variation (CV)0.66942613
Kurtosis-0.37837472
Mean8.1499152
Median Absolute Deviation (MAD)3.96
Skewness-0.37391665
Sum42640201
Variance29.765385
MonotonicityNot monotonic
2023-04-19T11:58:05.548291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.39 9754
 
0.2%
8.89 9611
 
0.2%
9.28 9371
 
0.2%
9.22 9258
 
0.2%
9.11 9239
 
0.2%
9.5 9168
 
0.2%
9.61 9112
 
0.2%
6.61 8691
 
0.2%
9.78 8648
 
0.2%
9.72 8647
 
0.2%
Other values (3550) 5140482
92.7%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
-12.6 3
< 0.1%
-12.5 2
< 0.1%
-12.4 1
 
< 0.1%
-12.39 1
 
< 0.1%
-12.34 1
 
< 0.1%
-12.33 2
< 0.1%
-12.3 2
< 0.1%
-12.29 1
 
< 0.1%
-12.28 2
< 0.1%
-12.25 2
< 0.1%
ValueCountFrequency (%)
59.78 10
< 0.1%
59.32 1
 
< 0.1%
59.05 1
 
< 0.1%
58.72 2
 
< 0.1%
41.61 1
 
< 0.1%
41.56 1
 
< 0.1%
34.39 1
 
< 0.1%
32.36 1
 
< 0.1%
30.23 1
 
< 0.1%
29.16 1
 
< 0.1%

LC_n
Real number (ℝ)

Distinct47
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean35.381164
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:05.604427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q137
median37
Q338
95-th percentile38
Maximum47
Range46
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.6904014
Coefficient of variation (CV)0.18909501
Kurtosis12.765319
Mean35.381164
Median Absolute Deviation (MAD)1
Skewness-3.6654634
Sum1.8511358 × 108
Variance44.761471
MonotonicityNot monotonic
2023-04-19T11:58:05.656031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
37 2148882
38.7%
38 2133692
38.5%
35 168968
 
3.0%
32 152968
 
2.8%
36 152725
 
2.8%
31 74293
 
1.3%
10 19945
 
0.4%
30 19039
 
0.3%
3 18727
 
0.3%
8 18494
 
0.3%
Other values (37) 324248
 
5.8%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
1 11336
0.2%
2 18208
0.3%
3 18727
0.3%
4 17387
0.3%
5 17305
0.3%
6 16371
0.3%
7 16408
0.3%
8 18494
0.3%
9 16379
0.3%
10 19945
0.4%
ValueCountFrequency (%)
47 4
 
< 0.1%
46 1
 
< 0.1%
45 7
 
< 0.1%
44 11
 
< 0.1%
43 15
 
< 0.1%
42 22
 
< 0.1%
41 28
 
< 0.1%
40 38
 
< 0.1%
39 3399
 
0.1%
38 2133692
38.5%

LC_RAD
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct926
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean79.722921
Minimum0
Maximum1017
Zeros2610449
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:05.709016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q385
95-th percentile447
Maximum1017
Range1017
Interquartile range (IQR)85

Descriptive statistics

Standard deviation144.51869
Coefficient of variation (CV)1.8127621
Kurtosis4.1749581
Mean79.722921
Median Absolute Deviation (MAD)1
Skewness2.198542
Sum4.1710881 × 108
Variance20885.651
MonotonicityNot monotonic
2023-04-19T11:58:05.766675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2610449
47.1%
1 44743
 
0.8%
4 37411
 
0.7%
3 33443
 
0.6%
2 31940
 
0.6%
5 27484
 
0.5%
6 22004
 
0.4%
7 20077
 
0.4%
23 19410
 
0.3%
22 19381
 
0.3%
Other values (916) 2365639
42.6%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
0 2610449
47.1%
1 44743
 
0.8%
2 31940
 
0.6%
3 33443
 
0.6%
4 37411
 
0.7%
5 27484
 
0.5%
6 22004
 
0.4%
7 20077
 
0.4%
8 18710
 
0.3%
9 17207
 
0.3%
ValueCountFrequency (%)
1017 1
< 0.1%
994 1
< 0.1%
975 1
< 0.1%
959 1
< 0.1%
956 1
< 0.1%
954 1
< 0.1%
950 2
< 0.1%
949 1
< 0.1%
948 2
< 0.1%
946 1
< 0.1%

LC_RAININ
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct110
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.00010391016
Minimum0
Maximum0.38
Zeros5068067
Zeros (%)91.4%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:05.821584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.38
Range0.38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0010074327
Coefficient of variation (CV)9.6952279
Kurtosis8050.1865
Mean0.00010391016
Median Absolute Deviation (MAD)0
Skewness46.73705
Sum543.656
Variance1.0149207 × 10-6
MonotonicityNot monotonic
2023-04-19T11:58:05.876310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5068067
91.4%
0.002 52715
 
1.0%
0.001 45753
 
0.8%
0.003 26337
 
0.5%
0.004 11488
 
0.2%
0.005 6790
 
0.1%
0.006 5099
 
0.1%
0.007 3265
 
0.1%
0.008 2432
 
< 0.1%
0.009 1681
 
< 0.1%
Other values (100) 8354
 
0.2%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
0 5068067
91.4%
0.001 45753
 
0.8%
0.002 52715
 
1.0%
0.003 26337
 
0.5%
0.004 11488
 
0.2%
0.005 6790
 
0.1%
0.006 5099
 
0.1%
0.007 3265
 
0.1%
0.008 2432
 
< 0.1%
0.009 1681
 
< 0.1%
ValueCountFrequency (%)
0.38 1
< 0.1%
0.335 1
< 0.1%
0.167 1
< 0.1%
0.141 2
< 0.1%
0.129 1
< 0.1%
0.123 2
< 0.1%
0.114 1
< 0.1%
0.111 1
< 0.1%
0.11 1
< 0.1%
0.109 1
< 0.1%

LC_DAILYRAIN
Real number (ℝ)

MISSING  ZEROS 

Distinct144
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.0012207363
Minimum0
Maximum0.154
Zeros4333746
Zeros (%)78.1%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:05.933397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.007
Maximum0.154
Range0.154
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0051048583
Coefficient of variation (CV)4.1817864
Kurtosis337.27104
Mean0.0012207363
Median Absolute Deviation (MAD)0
Skewness14.203188
Sum6386.869
Variance2.6059579 × 10-5
MonotonicityNot monotonic
2023-04-19T11:58:05.987271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4333746
78.1%
0.002 181737
 
3.3%
0.001 118642
 
2.1%
0.003 81847
 
1.5%
0.004 80846
 
1.5%
0.007 72514
 
1.3%
0.005 60622
 
1.1%
0.006 54105
 
1.0%
0.008 36966
 
0.7%
0.009 28636
 
0.5%
Other values (134) 182320
 
3.3%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
0 4333746
78.1%
0.001 118642
 
2.1%
0.002 181737
 
3.3%
0.003 81847
 
1.5%
0.004 80846
 
1.5%
0.005 60622
 
1.1%
0.006 54105
 
1.0%
0.007 72514
 
1.3%
0.008 36966
 
0.7%
0.009 28636
 
0.5%
ValueCountFrequency (%)
0.154 7
 
< 0.1%
0.153 14
 
< 0.1%
0.152 97
 
< 0.1%
0.151 789
< 0.1%
0.15 116
 
< 0.1%
0.149 15
 
< 0.1%
0.148 10
 
< 0.1%
0.147 5
 
< 0.1%
0.146 13
 
< 0.1%
0.145 9
 
< 0.1%

LC_WINDDIR
Real number (ℝ)

MISSING  ZEROS 

Distinct360
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean-5.7257561
Minimum-179
Maximum180
Zeros2022619
Zeros (%)36.5%
Negative1714369
Negative (%)30.9%
Memory size42.3 MiB
2023-04-19T11:58:06.045705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-179
5-th percentile-158
Q1-56
median0
Q328
95-th percentile154
Maximum180
Range359
Interquartile range (IQR)84

Descriptive statistics

Standard deviation87.312361
Coefficient of variation (CV)-15.249054
Kurtosis-0.34460102
Mean-5.7257561
Median Absolute Deviation (MAD)41
Skewness0.026313425
Sum-29957047
Variance7623.4484
MonotonicityNot monotonic
2023-04-19T11:58:06.097384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2022619
36.5%
-146 13778
 
0.2%
-147 13770
 
0.2%
-144 13767
 
0.2%
-145 13690
 
0.2%
-143 13621
 
0.2%
-148 13606
 
0.2%
-149 13440
 
0.2%
-142 13402
 
0.2%
-140 13355
 
0.2%
Other values (350) 3086933
55.7%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
-179 10578
0.2%
-178 10785
0.2%
-177 11081
0.2%
-176 10955
0.2%
-175 11332
0.2%
-174 11363
0.2%
-173 11644
0.2%
-172 11870
0.2%
-171 11991
0.2%
-170 12096
0.2%
ValueCountFrequency (%)
180 10609
0.2%
179 10390
0.2%
178 10234
0.2%
177 10109
0.2%
176 9867
0.2%
175 9803
0.2%
174 9685
0.2%
173 9745
0.2%
172 9771
0.2%
171 9676
0.2%

LC_WINDSPEED
Real number (ℝ)

MISSING  ZEROS 

Distinct892
Distinct (%)< 0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.28557705
Minimum0
Maximum13.7
Zeros2122964
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:06.154139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.04
Q30.31
95-th percentile1.4
Maximum13.7
Range13.7
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.56429441
Coefficient of variation (CV)1.9759795
Kurtosis20.145078
Mean0.28557705
Median Absolute Deviation (MAD)0.04
Skewness3.7023949
Sum1494133.7
Variance0.31842819
MonotonicityNot monotonic
2023-04-19T11:58:06.205720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2122964
38.3%
0.01 186275
 
3.4%
0.02 153706
 
2.8%
0.03 122616
 
2.2%
0.04 105635
 
1.9%
0.05 93342
 
1.7%
0.06 84198
 
1.5%
0.07 76651
 
1.4%
0.08 70054
 
1.3%
0.09 65109
 
1.2%
Other values (882) 2151431
38.8%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
0 2122964
38.3%
0.01 186275
 
3.4%
0.02 153706
 
2.8%
0.03 122616
 
2.2%
0.04 105635
 
1.9%
0.05 93342
 
1.7%
0.06 84198
 
1.5%
0.07 76651
 
1.4%
0.08 70054
 
1.3%
0.09 65109
 
1.2%
ValueCountFrequency (%)
13.7 1
< 0.1%
12.2 1
< 0.1%
12.15 1
< 0.1%
11.44 1
< 0.1%
11.21 1
< 0.1%
11.2 1
< 0.1%
10.71 1
< 0.1%
10.62 1
< 0.1%
10.45 1
< 0.1%
10.42 1
< 0.1%

Date
Categorical

Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.3 MiB
2022-07-03
 
15552
2022-09-08
 
15552
2022-09-06
 
15552
2022-09-05
 
15552
2022-09-04
 
15552
Other values (361)
5469120 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters55468800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-01-01
2nd row2022-01-01
3rd row2022-01-01
4th row2022-01-01
5th row2022-01-01

Common Values

ValueCountFrequency (%)
2022-07-03 15552
 
0.3%
2022-09-08 15552
 
0.3%
2022-09-06 15552
 
0.3%
2022-09-05 15552
 
0.3%
2022-09-04 15552
 
0.3%
2022-09-03 15552
 
0.3%
2022-09-02 15552
 
0.3%
2022-09-01 15552
 
0.3%
2022-08-31 15552
 
0.3%
2022-08-30 15552
 
0.3%
Other values (356) 5391360
97.2%

Length

2023-04-19T11:58:06.254738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-07-03 15552
 
0.3%
2022-07-02 15552
 
0.3%
2022-04-29 15552
 
0.3%
2022-04-28 15552
 
0.3%
2022-04-27 15552
 
0.3%
2022-04-26 15552
 
0.3%
2022-09-08 15552
 
0.3%
2022-07-01 15552
 
0.3%
2022-07-04 15552
 
0.3%
2022-04-23 15552
 
0.3%
Other values (356) 5391360
97.2%

Most occurring characters

ValueCountFrequency (%)
2 19873620
35.8%
0 12318624
22.2%
- 11093760
20.0%
1 4810762
 
8.7%
3 1258668
 
2.3%
7 1029024
 
1.9%
8 1029024
 
1.9%
5 1029024
 
1.9%
6 1013472
 
1.8%
4 1013462
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44375040
80.0%
Dash Punctuation 11093760
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19873620
44.8%
0 12318624
27.8%
1 4810762
 
10.8%
3 1258668
 
2.8%
7 1029024
 
2.3%
8 1029024
 
2.3%
5 1029024
 
2.3%
6 1013472
 
2.3%
4 1013462
 
2.3%
9 999360
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 11093760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55468800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 19873620
35.8%
0 12318624
22.2%
- 11093760
20.0%
1 4810762
 
8.7%
3 1258668
 
2.3%
7 1029024
 
1.9%
8 1029024
 
1.9%
5 1029024
 
1.9%
6 1013472
 
1.8%
4 1013462
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55468800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 19873620
35.8%
0 12318624
22.2%
- 11093760
20.0%
1 4810762
 
8.7%
3 1258668
 
2.3%
7 1029024
 
1.9%
8 1029024
 
1.9%
5 1029024
 
1.9%
6 1013472
 
1.8%
4 1013462
 
1.8%

Year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.3 MiB
2022
5546772 
2023
 
108

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters22187520
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 5546772
> 99.9%
2023 108
 
< 0.1%

Length

2023-04-19T11:58:06.296472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T11:58:06.349846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2022 5546772
> 99.9%
2023 108
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 16640532
75.0%
0 5546880
 
25.0%
3 108
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22187520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 16640532
75.0%
0 5546880
 
25.0%
3 108
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22187520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 16640532
75.0%
0 5546880
 
25.0%
3 108
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22187520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 16640532
75.0%
0 5546880
 
25.0%
3 108
 
< 0.1%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6317703
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:06.386482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4146123
Coefficient of variation (CV)0.514887
Kurtosis-1.179743
Mean6.6317703
Median Absolute Deviation (MAD)3
Skewness-0.044056215
Sum36785634
Variance11.659577
MonotonicityNot monotonic
2023-04-19T11:58:06.424472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 482112
8.7%
7 482112
8.7%
8 482112
8.7%
10 482112
8.7%
12 482112
8.7%
6 466560
8.4%
9 466560
8.4%
11 466560
8.4%
4 466550
8.4%
1 437482
7.9%
Other values (2) 832608
15.0%
ValueCountFrequency (%)
1 437482
7.9%
2 395136
7.1%
3 437472
7.9%
4 466550
8.4%
5 482112
8.7%
6 466560
8.4%
7 482112
8.7%
8 482112
8.7%
9 466560
8.4%
10 482112
8.7%
ValueCountFrequency (%)
12 482112
8.7%
11 466560
8.4%
10 482112
8.7%
9 466560
8.4%
8 482112
8.7%
7 482112
8.7%
6 466560
8.4%
5 482112
8.7%
4 466550
8.4%
3 437472
7.9%

Day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.724922
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:06.470295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7981664
Coefficient of variation (CV)0.55950461
Kurtosis-1.1935608
Mean15.724922
Median Absolute Deviation (MAD)8
Skewness0.0071842322
Sum87224256
Variance77.407731
MonotonicityNot monotonic
2023-04-19T11:58:06.517637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 182304
 
3.3%
2 182304
 
3.3%
28 182304
 
3.3%
27 182304
 
3.3%
26 182304
 
3.3%
25 182304
 
3.3%
24 182304
 
3.3%
23 182304
 
3.3%
22 182304
 
3.3%
21 182304
 
3.3%
Other values (21) 3723840
67.1%
ValueCountFrequency (%)
1 182304
3.3%
2 182304
3.3%
3 182304
3.3%
4 182304
3.3%
5 182304
3.3%
6 182304
3.3%
7 182304
3.3%
8 182304
3.3%
9 182304
3.3%
10 182304
3.3%
ValueCountFrequency (%)
31 105984
1.9%
30 168192
3.0%
29 168192
3.0%
28 182304
3.3%
27 182304
3.3%
26 182304
3.3%
25 182304
3.3%
24 182304
3.3%
23 182304
3.3%
22 182304
3.3%

Hour
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros231120
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:06.565681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9221872
Coefficient of variation (CV)0.60192932
Kurtosis-1.2041739
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum63789120
Variance47.916675
MonotonicityNot monotonic
2023-04-19T11:58:06.614218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 231120
 
4.2%
1 231120
 
4.2%
22 231120
 
4.2%
21 231120
 
4.2%
20 231120
 
4.2%
19 231120
 
4.2%
18 231120
 
4.2%
17 231120
 
4.2%
16 231120
 
4.2%
15 231120
 
4.2%
Other values (14) 3235680
58.3%
ValueCountFrequency (%)
0 231120
4.2%
1 231120
4.2%
2 231120
4.2%
3 231120
4.2%
4 231120
4.2%
5 231120
4.2%
6 231120
4.2%
7 231120
4.2%
8 231120
4.2%
9 231120
4.2%
ValueCountFrequency (%)
23 231120
4.2%
22 231120
4.2%
21 231120
4.2%
20 231120
4.2%
19 231120
4.2%
18 231120
4.2%
17 231120
4.2%
16 231120
4.2%
15 231120
4.2%
14 231120
4.2%

Minute
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25
Minimum0
Maximum50
Zeros924480
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:06.651937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median25
Q340
95-th percentile50
Maximum50
Range50
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.078253
Coefficient of variation (CV)0.68313011
Kurtosis-1.2685715
Mean25
Median Absolute Deviation (MAD)15
Skewness0
Sum1.38672 × 108
Variance291.66672
MonotonicityNot monotonic
2023-04-19T11:58:06.780057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10 924480
16.7%
20 924480
16.7%
30 924480
16.7%
40 924480
16.7%
50 924480
16.7%
0 924480
16.7%
ValueCountFrequency (%)
0 924480
16.7%
10 924480
16.7%
20 924480
16.7%
30 924480
16.7%
40 924480
16.7%
50 924480
16.7%
ValueCountFrequency (%)
50 924480
16.7%
40 924480
16.7%
30 924480
16.7%
20 924480
16.7%
10 924480
16.7%
0 924480
16.7%

LC_RAD60
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct868
Distinct (%)< 0.1%
Missing277022
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean79.6632
Minimum0
Maximum914
Zeros2542931
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size42.3 MiB
2023-04-19T11:58:06.828668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q388
95-th percentile435
Maximum914
Range914
Interquartile range (IQR)88

Descriptive statistics

Standard deviation141.71334
Coefficient of variation (CV)1.7789059
Kurtosis3.8493839
Mean79.6632
Median Absolute Deviation (MAD)2
Skewness2.1298608
Sum4.1981375 × 108
Variance20082.67
MonotonicityNot monotonic
2023-04-19T11:58:06.881832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2542931
45.8%
1 82486
 
1.5%
2 49595
 
0.9%
3 41647
 
0.8%
4 39827
 
0.7%
5 26770
 
0.5%
6 22505
 
0.4%
7 20735
 
0.4%
8 18928
 
0.3%
27 18481
 
0.3%
Other values (858) 2405953
43.4%
(Missing) 277022
 
5.0%
ValueCountFrequency (%)
0 2542931
45.8%
1 82486
 
1.5%
2 49595
 
0.9%
3 41647
 
0.8%
4 39827
 
0.7%
5 26770
 
0.5%
6 22505
 
0.4%
7 20735
 
0.4%
8 18928
 
0.3%
9 18414
 
0.3%
ValueCountFrequency (%)
914 1
< 0.1%
900 1
< 0.1%
896 1
< 0.1%
894 1
< 0.1%
884 1
< 0.1%
882 2
< 0.1%
878 1
< 0.1%
876 1
< 0.1%
873 1
< 0.1%
870 1
< 0.1%

LC_TEMP_QCL0
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5349
Distinct (%)0.1%
Missing314899
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean12.777789
Minimum-11.7
Maximum60
Zeros7227
Zeros (%)0.1%
Negative175303
Negative (%)3.2%
Memory size42.3 MiB
2023-04-19T11:58:06.937254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.7
5-th percentile1
Q17.38
median12.39
Q317.89
95-th percentile25.77
Maximum60
Range71.7
Interquartile range (IQR)10.51

Descriptive statistics

Standard deviation7.6258419
Coefficient of variation (CV)0.59680448
Kurtosis-0.15275933
Mean12.777789
Median Absolute Deviation (MAD)5.28
Skewness0.20711242
Sum66853152
Variance58.153465
MonotonicityNot monotonic
2023-04-19T11:58:06.989680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.89 11822
 
0.2%
9.78 11787
 
0.2%
10 11707
 
0.2%
10.5 11097
 
0.2%
9.72 11073
 
0.2%
10.11 10755
 
0.2%
10.28 10739
 
0.2%
10.22 10696
 
0.2%
8.28 10695
 
0.2%
9.61 10655
 
0.2%
Other values (5339) 5120955
92.3%
(Missing) 314899
 
5.7%
ValueCountFrequency (%)
-11.7 3
< 0.1%
-11.66 2
< 0.1%
-11.61 3
< 0.1%
-11.6 2
< 0.1%
-11.56 1
 
< 0.1%
-11.55 1
 
< 0.1%
-11.54 1
 
< 0.1%
-11.5 4
< 0.1%
-11.47 1
 
< 0.1%
-11.45 2
< 0.1%
ValueCountFrequency (%)
60 10
< 0.1%
59.5 1
 
< 0.1%
59.28 1
 
< 0.1%
58.89 2
 
< 0.1%
43.76 1
 
< 0.1%
43.71 1
 
< 0.1%
43.53 1
 
< 0.1%
43.5 2
 
< 0.1%
43.49 1
 
< 0.1%
43.44 1
 
< 0.1%

LC_TEMP_QCL1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5326
Distinct (%)0.1%
Missing345487
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean12.76951
Minimum-11.7
Maximum43.11
Zeros7132
Zeros (%)0.1%
Negative174851
Negative (%)3.2%
Memory size42.3 MiB
2023-04-19T11:58:07.046026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.7
5-th percentile1
Q17.37
median12.39
Q317.89
95-th percentile25.75
Maximum43.11
Range54.81
Interquartile range (IQR)10.52

Descriptive statistics

Standard deviation7.6205306
Coefficient of variation (CV)0.59677551
Kurtosis-0.15715431
Mean12.76951
Median Absolute Deviation (MAD)5.28
Skewness0.20437188
Sum66419238
Variance58.072487
MonotonicityNot monotonic
2023-04-19T11:58:07.096944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.89 11756
 
0.2%
9.78 11678
 
0.2%
10 11612
 
0.2%
10.5 11029
 
0.2%
9.72 11006
 
0.2%
10.28 10660
 
0.2%
10.11 10659
 
0.2%
8.28 10633
 
0.2%
10.22 10626
 
0.2%
9.61 10570
 
0.2%
Other values (5316) 5091164
91.8%
(Missing) 345487
 
6.2%
ValueCountFrequency (%)
-11.7 3
< 0.1%
-11.66 2
< 0.1%
-11.61 3
< 0.1%
-11.6 2
< 0.1%
-11.56 1
 
< 0.1%
-11.55 1
 
< 0.1%
-11.54 1
 
< 0.1%
-11.5 4
< 0.1%
-11.47 1
 
< 0.1%
-11.45 2
< 0.1%
ValueCountFrequency (%)
43.11 1
< 0.1%
42.94 1
< 0.1%
42.87 1
< 0.1%
42.83 1
< 0.1%
42.69 1
< 0.1%
42.62 1
< 0.1%
42.51 1
< 0.1%
42.48 1
< 0.1%
42.44 1
< 0.1%
42.43 1
< 0.1%

LC_TEMP_QCL2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct92170
Distinct (%)1.8%
Missing345487
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean12.771063
Minimum-11.7
Maximum43.197
Zeros67
Zeros (%)< 0.1%
Negative177664
Negative (%)3.2%
Memory size42.3 MiB
2023-04-19T11:58:07.152760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.7
5-th percentile0.9925
Q17.37
median12.4
Q317.89
95-th percentile25.761
Maximum43.197
Range54.897
Interquartile range (IQR)10.52

Descriptive statistics

Standard deviation7.6199829
Coefficient of variation (CV)0.59666001
Kurtosis-0.15828887
Mean12.771063
Median Absolute Deviation (MAD)5.276
Skewness0.20587398
Sum66427320
Variance58.064139
MonotonicityNot monotonic
2023-04-19T11:58:07.203700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 1748
 
< 0.1%
10.3 1719
 
< 0.1%
10.1 1719
 
< 0.1%
11.8 1648
 
< 0.1%
13.3 1627
 
< 0.1%
10.2 1608
 
< 0.1%
10.7 1544
 
< 0.1%
13.2 1499
 
< 0.1%
11.1 1467
 
< 0.1%
11.3 1464
 
< 0.1%
Other values (92160) 5185350
93.5%
(Missing) 345487
 
6.2%
ValueCountFrequency (%)
-11.7 2
< 0.1%
-11.6 1
 
< 0.1%
-11.5 2
< 0.1%
-11.4655 1
 
< 0.1%
-11.4345 2
< 0.1%
-11.4 2
< 0.1%
-11.3845 3
< 0.1%
-11.3555 1
 
< 0.1%
-11.3245 1
 
< 0.1%
-11.3145 1
 
< 0.1%
ValueCountFrequency (%)
43.197 1
< 0.1%
43.027 1
< 0.1%
42.929 1
< 0.1%
42.917 1
< 0.1%
42.749 1
< 0.1%
42.707 1
< 0.1%
42.542 1
< 0.1%
42.539 2
< 0.1%
42.522 1
< 0.1%
42.519 1
< 0.1%

LC_TEMP_QCL3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4567782
Distinct (%)87.8%
Missing345487
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean12.606175
Minimum-11.4
Maximum42.161164
Zeros0
Zeros (%)0.0%
Negative177244
Negative (%)3.2%
Memory size42.3 MiB
2023-04-19T11:58:07.281911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.4
5-th percentile1.0325705
Q17.387517
median12.349102
Q317.66019
95-th percentile25.004656
Maximum42.161164
Range53.561164
Interquartile range (IQR)10.272673

Descriptive statistics

Standard deviation7.3911548
Coefficient of variation (CV)0.58631224
Kurtosis-0.18548579
Mean12.606175
Median Absolute Deviation (MAD)5.1488284
Skewness0.1501703
Sum65569671
Variance54.62917
MonotonicityNot monotonic
2023-04-19T11:58:07.338302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 1400
 
< 0.1%
10.2 1360
 
< 0.1%
11.9 1335
 
< 0.1%
10.3 1334
 
< 0.1%
10.9 1333
 
< 0.1%
13.3 1333
 
< 0.1%
13.4 1310
 
< 0.1%
11.3 1288
 
< 0.1%
13.6 1282
 
< 0.1%
10.1 1281
 
< 0.1%
Other values (4567772) 5188137
93.5%
(Missing) 345487
 
6.2%
ValueCountFrequency (%)
-11.4 3
< 0.1%
-11.3 2
< 0.1%
-11.2 1
 
< 0.1%
-11.18447 1
 
< 0.1%
-11.17442 2
< 0.1%
-11.12442 1
 
< 0.1%
-11.1 1
 
< 0.1%
-11.09022 2
< 0.1%
-11.07447 1
 
< 0.1%
-11.05952 1
 
< 0.1%
ValueCountFrequency (%)
42.16116354 1
< 0.1%
42.12416033 1
< 0.1%
42.02266235 1
< 0.1%
41.95165925 1
< 0.1%
41.93676939 1
< 0.1%
41.85961032 1
< 0.1%
41.83010052 1
< 0.1%
41.70123848 1
< 0.1%
41.36824861 1
< 0.1%
41.23725765 1
< 0.1%

Interactions

2023-04-19T11:57:29.517093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:42.461381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:49.114689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:55.859734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:02.683871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:09.678055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:16.476602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:23.003075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:29.783092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:36.460331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:43.013771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:50.096231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:56.684262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:03.398793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:10.077976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:16.695364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:23.253316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:29.869187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:42.911173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:49.456351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:56.229415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:03.042842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:10.084296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:16.940956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:23.391232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:30.148917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:36.856583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:43.436459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:50.510567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:57.070607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:03.756692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:10.496748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:17.051100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:23.608087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:30.232571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:43.273110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:49.829917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:56.566729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:03.419706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:10.446144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:17.380171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:23.790479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:30.536620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:37.232019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:43.856019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:50.909852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:57.461290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:04.118047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:10.890543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:17.406412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:23.981461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:30.583611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:43.652300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:50.203465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:56.938611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:03.768580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:10.795702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:17.756029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:24.162900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:30.921636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:37.609902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:44.244766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:51.297944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:57.854627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:04.494319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:11.256245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:17.790356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:24.339244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:30.955742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:44.035255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:50.565501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:57.323156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:04.149614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:11.125880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:18.127248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:24.557392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:31.315476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:37.983862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:44.666720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:51.678027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:58.365688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:04.859084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:11.618784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:18.180629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:24.713196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:31.327641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:44.401475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:50.975211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:57.712947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:04.546478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:11.486231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:18.484511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:24.947107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:31.719917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:38.368215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:45.126189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:52.062171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:58.767981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:05.243801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:11.984384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:18.613208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:25.088540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:31.698880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:44.815437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:51.367379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:58.117478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:05.018063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:11.874218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:18.864593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:25.307822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:32.098467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:38.774838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:45.597495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:52.534042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:59.176594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:05.615314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:12.375984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:19.013723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:25.458988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:32.063019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:45.213658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:51.723179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:58.550687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:05.440423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:12.277104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:19.233956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:25.702370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:32.463952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:39.159440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:46.117306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:52.921723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:59.565950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:05.980816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:12.797752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:19.400039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:25.823658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:32.467506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:45.630848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:52.106680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:59.022007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:05.984954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:12.680630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:19.608626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:26.112113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:32.869074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:39.563075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:46.577470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:53.349746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:00.002745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:06.446481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:13.296564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:19.912679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:26.232075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:32.835581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:46.037064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:52.476021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:59.413414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:06.426133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:13.040852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:19.985350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:26.496096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:33.261371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:39.901673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:46.915593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:53.708608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:00.342159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:06.829115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:13.723617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:20.283727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:26.609163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:33.205847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:46.446078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:52.849354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:59.816253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:06.846193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:13.437530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:20.374927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:26.939717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:33.648575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:40.257361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:47.335830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:54.016353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:00.695142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:07.212711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:14.167009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:20.685366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:26.989127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:33.571386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:46.829978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:53.296187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:00.218980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:07.267909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:13.820381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:20.746563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:27.338005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:34.037098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:40.579233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:47.696315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:54.350120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:01.011302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:07.597839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:14.556742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:21.055312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:27.375043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:33.944313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:47.212765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:53.911535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:00.606970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:07.708172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:14.225896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:21.116310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:27.756979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:34.412168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:40.978543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:48.120293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:54.762313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:01.434909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:07.937456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:14.926026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:21.454222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:27.748930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:34.300889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:47.593215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:54.323717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:01.039863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:08.099163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:14.613529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:21.477087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:28.150704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:34.886763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:41.386501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:48.488892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:55.138590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:01.818876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:08.306135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:15.267866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:21.816721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:28.108580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:34.642485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:47.973078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:54.689762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:01.495216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:08.513202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:15.147948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:21.829408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:28.569390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:35.264664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:41.790224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:48.862175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:55.509559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:02.204960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:08.667308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:15.635306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:22.160146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:28.458858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:35.029463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:48.345029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:55.067125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:01.879694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:08.895480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:15.558885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:22.212188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:28.958371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:35.631361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:42.176773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:49.242448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:55.888670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:02.602865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:09.092653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:15.987983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:22.516020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:28.789995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:35.364684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:48.711751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:55:55.429896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:02.268897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:09.278630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:16.011036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:22.583034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:29.343652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:36.016713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:42.580681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:49.650740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:56:56.258959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:02.989796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:09.569749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:16.334853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:22.884263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T11:57:29.151727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-19T11:58:07.428294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
LC_HUMIDITYLC_DWPTEMPLC_nLC_RADLC_RAININLC_DAILYRAINLC_WINDDIRLC_WINDSPEEDMonthDayHourMinuteLC_RAD60LC_TEMP_QCL0LC_TEMP_QCL1LC_TEMP_QCL2LC_TEMP_QCL3Year
LC_HUMIDITY1.000-0.1690.015-0.5530.1780.262-0.048-0.2920.2010.033-0.218-0.000-0.608-0.641-0.641-0.638-0.6250.012
LC_DWPTEMP-0.1691.000-0.0050.2250.0430.079-0.027-0.0080.3120.0730.051-0.0000.2410.8300.8300.8310.8400.005
LC_n0.015-0.0051.000-0.0100.001-0.006-0.004-0.0170.0100.0070.0030.133-0.010-0.009-0.009-0.008-0.0080.004
LC_RAD-0.5530.225-0.0101.000-0.053-0.052-0.0010.348-0.067-0.001-0.008-0.0000.9540.4950.4950.4940.4820.002
LC_RAININ0.1780.0430.001-0.0531.0000.360-0.0200.0610.0410.0030.020-0.004-0.048-0.048-0.048-0.048-0.0460.000
LC_DAILYRAIN0.2620.079-0.006-0.0520.3601.000-0.0660.1350.1000.0290.1580.001-0.035-0.072-0.072-0.071-0.0680.000
LC_WINDDIR-0.048-0.027-0.004-0.001-0.020-0.0661.000-0.0920.0220.0100.0000.000-0.0010.0140.0140.0140.0120.004
LC_WINDSPEED-0.292-0.008-0.0170.3480.0610.135-0.0921.000-0.084-0.0230.0540.0010.3540.1410.1420.1400.1360.008
Month0.2010.3120.010-0.0670.0410.1000.022-0.0841.0000.0120.0000.000-0.0680.1450.1460.1470.1540.010
Day0.0330.0730.007-0.0010.0030.0290.010-0.0230.0121.0000.0000.000-0.0010.0450.0450.0450.0450.011
Hour-0.2180.0510.003-0.0080.0200.1580.0000.0540.0000.0001.0000.0000.0800.1600.1600.1600.1580.012
Minute-0.000-0.0000.133-0.000-0.0040.0010.0000.0010.0000.0000.0001.000-0.001-0.0000.0000.000-0.0000.010
LC_RAD60-0.6080.241-0.0100.954-0.048-0.035-0.0010.354-0.068-0.0010.080-0.0011.0000.5370.5370.5370.5220.002
LC_TEMP_QCL0-0.6410.830-0.0090.495-0.048-0.0720.0140.1410.1450.0450.160-0.0000.5371.0001.0001.0000.9990.006
LC_TEMP_QCL1-0.6410.830-0.0090.495-0.048-0.0720.0140.1420.1460.0450.1600.0000.5371.0001.0001.0000.9990.008
LC_TEMP_QCL2-0.6380.831-0.0080.494-0.048-0.0710.0140.1400.1470.0450.1600.0000.5371.0001.0001.0000.9990.008
LC_TEMP_QCL3-0.6250.840-0.0080.482-0.046-0.0680.0120.1360.1540.0450.158-0.0000.5220.9990.9990.9991.0000.008
Year0.0120.0050.0040.0020.0000.0000.0040.0080.0100.0110.0120.0100.0020.0060.0080.0080.0081.000

Missing values

2023-04-19T11:57:36.651640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-19T11:57:43.734933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-19T11:58:01.847741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DATEUTCIDLC_HUMIDITYLC_DWPTEMPLC_nLC_RADLC_RAININLC_DAILYRAINLC_WINDDIRLC_WINDSPEEDDateYearMonthDayHourMinuteLC_RAD60LC_TEMP_QCL0LC_TEMP_QCL1LC_TEMP_QCL2LC_TEMP_QCL3
02022-01-01 00:10:00LC-00292.011.7838.00.00.00.0-169.00.432022-01-012022110100.013.1113.1113.051513.048027
12022-01-01 00:20:00LC-00292.011.7337.00.00.00.0-170.00.332022-01-012022110200.013.0113.0112.951512.985849
22022-01-01 00:30:00LC-00292.011.7338.00.00.00.0-167.00.462022-01-012022110300.013.0013.0012.941512.950322
32022-01-01 00:40:00LC-00292.011.7237.00.00.00.0-160.00.522022-01-012022110400.013.0013.0012.941512.949550
42022-01-01 00:50:00LC-00292.011.7238.00.00.00.0-166.00.512022-01-012022110500.013.0013.0012.941512.952268
52022-01-01 01:00:00LC-00292.011.7237.00.00.00.0-158.00.932022-01-01202211100.013.0013.0012.941512.938731
62022-01-01 01:10:00LC-00292.011.7138.00.00.00.0-161.00.542022-01-012022111100.013.0013.0012.941512.949960
72022-01-01 01:20:00LC-00291.011.6237.00.00.00.0-163.00.712022-01-012022111200.013.0013.0012.941512.960576
82022-01-01 01:30:00LC-00291.011.6138.00.00.00.0-160.00.542022-01-012022111300.013.0013.0012.941512.980432
92022-01-01 01:40:00LC-00291.011.6137.00.00.00.0-163.00.852022-01-012022111400.013.0013.0012.941512.963181
DATEUTCIDLC_HUMIDITYLC_DWPTEMPLC_nLC_RADLC_RAININLC_DAILYRAINLC_WINDDIRLC_WINDSPEEDDateYearMonthDayHourMinuteLC_RAD60LC_TEMP_QCL0LC_TEMP_QCL1LC_TEMP_QCL2LC_TEMP_QCL3
55468702022-12-31 22:30:00LC-13853.06.4932.00.00.00.002-70.01.452022-12-312022123122300.016.2016.2016.4516.39359
55468712022-12-31 22:40:00LC-13852.06.3231.00.00.00.002-52.01.232022-12-312022123122400.016.2916.2916.5416.48690
55468722022-12-31 22:50:00LC-13851.06.3032.00.00.00.002-64.01.042022-12-312022123122500.016.4316.4316.6816.63222
55468732022-12-31 23:00:00LC-13850.06.2131.00.00.00.002-61.01.432022-12-31202212312300.016.5016.5016.7516.71759
55468742022-12-31 23:10:00LC-13850.06.1632.00.00.00.002-62.01.372022-12-312022123123100.016.5616.5616.8116.77703
55468752022-12-31 23:20:00LC-13850.06.0532.00.00.00.000-49.00.672022-12-312022123123200.016.5116.5116.7616.76285
55468762022-12-31 23:30:00LC-13850.06.0032.00.00.00.000-65.01.392022-12-312022123123300.016.4816.4816.7316.70722
55468772022-12-31 23:40:00LC-13850.05.9031.00.00.00.000-41.01.252022-12-312022123123400.016.3716.3716.6216.60001
55468782022-12-31 23:50:00LC-13850.05.8932.00.00.00.000-51.01.222022-12-312022123123500.016.2716.2716.5216.50053
55468792023-01-01 00:00:00LC-13850.05.7831.00.00.00.000-53.01.082023-01-01202311000.016.1816.1816.4316.37461